Artificial intelligence-based wakeup word detection method and apparatus, device, and medium

A computing device, comprising a memory, a processor, and a plurality of computer programs stored in the memory that, when executed by the processor, cause the computing device to perform a plurality of operations including:

• constructing, by using a preset pronunciation dictionary, at least one syllable combination sequence for self-defined wakeup word text inputted by a user, the pronunciation dictionary comprising pronunciations respectively corresponding to a plurality of text elements, and the syllable combination sequence being an ordered combination of a plurality of syllables corresponding to a plurality of text elements of the wakeup word text;
• obtaining to-be-recognized speech data, and extracting speech features of speech frames in the speech data;
• inputting the speech features into a pre-constructed deep neural network (DNN) model, to output posterior probability vectors of the speech features corresponding to syllable identifiers, the DNN model comprising the same quantity of syllable output units as syllables of the pronunciation dictionary;
• determining a target probability vector from the posterior probability vectors according to the syllable combination sequence, the target probability vector comprising posterior probability values that are determined according to the posterior probability vectors and that correspond to the text elements in the wakeup word text;
• and
• calculating a confidence according to the target probability vector, and determining that the speech frames comprise the wakeup word text when the confidence is greater than or equal to a threshold.

A non-transitory computer-readable storage medium, storing a plurality of computer programs that, when executed by a processor of a computing device, cause the computing device to perform a plurality of operations including:

• constructing, by using a preset pronunciation dictionary, at least one syllable combination sequence for self-defined wakeup word text inputted by a user, the pronunciation dictionary comprising pronunciations respectively corresponding to a plurality of text elements, and the syllable combination sequence being an ordered combination of a plurality of syllables corresponding to a plurality of text elements of the wakeup word text;
• obtaining to-be-recognized speech data, and extracting speech features of speech frames in the speech data;
• inputting the speech features into a pre-constructed deep neural network (DNN) model, to output posterior probability vectors of the speech features corresponding to syllable identifiers, the DNN model comprising the same quantity of syllable output units as syllables of the pronunciation dictionary;
• determining a target probability vector from the posterior probability vectors according to the syllable combination sequence, the target probability vector comprising posterior probability values that are determined according to the posterior probability vectors and that correspond to the text elements in the wakeup word text;
• and
• calculating a confidence according to the target probability vector, and determining that the speech frames comprise the wakeup word text when the confidence is greater than or equal to a threshold.